Chikara Furusawa

Characterization of mesendoderm: a diverging point of the definitive endoderm and mesoderm in embryonic stem cell differentiation culture

Bipotent mesendoderm that can give rise to both endoderm and mesoderm is an established entity from C. elegans to zebrafish. Although previous studies in mouse embryo indicated the presence of bi-potent mesendoderm cells in the organizer region, characterization of mesendoderm and its differentiation processes are still unclear. As bi-potent mesendoderm is implicated as the major precursor of definitive endoderm, its identification is also essential for exploring the differentiation of definitive endoderm. In this study, we have established embryonic stem (ES) cell lines that carry GFP gene in the goosecoid (Gsc) gene locus and have investigated the differentiation course of mesendodermal cells using Gsc expression as a marker. Our results show that mesendoderm is represented as a Gsc-GFP+E-cadherin(ECD)+PDGFRα(αR)+ population and is selectively induced from ES cells under defined conditions containing either activin or nodal. Subsequently, it diverges to Gsc+ECD+αR- and Gsc+ECD-αR+ intermediates that eventually differentiate into definitive endoderm and mesodermal lineages, respectively. The presence of mesendodermal cells in nascent Gsc+ECD+αR+ population was also confirmed by single cell analysis. Finally, we show that the defined culture condition and surface markers developed in this study are applicable for obtaining pure mesendodermal cells and their immediate progenies from genetically unmanipulated ES cells.

The cyclic gene Hes1 contributes to diverse differentiation responses of embryonic stem cells

Stem cells do not all respond the same way, but the mechanisms underlying this heterogeneity are not well understood. Here, we found that expression of Hes1 and its downstream genes oscillate in mouse embryonic stem (ES) cells. Those expressing low and high levels of Hes1 tended to differentiate into neural and mesodermal cells, respectively. Furthermore, inactivation of Hes1 facilitated neural differentiation more uniformly at earlier time. Thus, Hes1-null ES cells display less heterogeneity in both the differentiation timing and fate choice, suggesting that the cyclic gene Hes1 contributes to heterogeneous responses of ES cells even under the same environmental conditions.

Comprehensive phenotypic analysis for identification of genes affecting growth under ethanol stress in Saccharomyces cerevisiae

We quantified the growth behavior of all available single gene deletion strains of budding yeast under ethanol stress. Genome-wide analyses enabled the extraction of the genes and determination of the functional categories required for growth under this condition. Statistical analyses revealed that the growth of 446 deletion strains under stress induced by 8% ethanol was defective. We classified these deleted genes into known functional categories, and found that many were important for growth under ethanol stress including several categories that have not been characterized, such as peroxisome. We also performed genome-wide screening under osmotic stress and identified 329 osmotic-sensitive strains. We excluded these strains from the 446 ethanol-sensitive strains to extract the genes whose deletion caused sensitivity to ethanol-specific (359 genes), osmotic-specific (242 genes), and both stresses (87 genes). We also extracted the functional categories that are specifically important for growth under ethanol stress. The genes and functional categories identified in the analysis might provide clues to improving ethanol stress tolerance among yeast cells.

Zipf's law in gene expression

Using data from gene expression databases on various organisms and tissues, including yeast, nematodes, human normal and cancer tissues, and embryonic stem cells, we found that the abundances of expressed genes exhibit a power-law distribution with an exponent close to -1; i.e., they obey Zipfs law. Furthermore, by simulations of a simple model with an intracellular reaction network, we found that Zipfs law of chemical abundance is a universal feature of cells where such a network optimizes the efficiency and faithfulness of self-reproduction. These findings provide novel insights into the nature of the organization of reaction dynamics in living cells.

Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

BackgroundIn silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates.ResultsThe reconstructed genome-scale metabolic model of C. glutamicum contains 502 reactions and 423 metabolites. We collected the reactions and biomass components from the database and literatures, and made the model available for the flux balance analysis by filling gaps in the reaction networks and removing inadequate loop reactions. Using the framework of FBA and our genome-scale metabolic model, we first simulated the changes in the metabolic flux profiles that occur on changing the oxygen uptake rate. The predicted production yields of carbon dioxide and organic acids agreed well with the experimental data. The metabolic profiles of amino acid production phases were also investigated. A comprehensive gene deletion study was performed in which the effects of gene deletions on metabolic fluxes were simulated; this helped in the identification of several genes whose deletion resulted in an improvement in organic acid production.ConclusionThe genome-scale metabolic model provides useful information for the evaluation of the metabolic capabilities and prediction of the metabolic characteristics of C. glutamicum. This can form a basis for the in silico design of C. glutamicum metabolic networks for improved bioproduction of desirable metabolites.

A Dynamical-Systems View of Stem Cell Biology

During development, cells undergo a unidirectional course of differentiation that progressively decreases the number of cell types they can potentially become. Stem cells, however, keep their potential to both proliferate and differentiate. A very important issue then is to understand the characteristics that distinguish stem cells from other cell types and allow them to conduct stable proliferation and differentiation. Here, we review relevant dynamical-systems approaches to describe the state transition between stem and differentiated cells, with an emphasis on fluctuating and oscillatory gene expression levels, as these represent the specific properties of stem cells. Relevance between recent experimental results and dynamical-systems descriptions of stem cell differentiation is also discussed.

Transcriptome analysis of parallel-evolved Escherichia coli strains under ethanol stress.

BackgroundUnderstanding ethanol tolerance in microorganisms is important for the improvement of bioethanol production. Hence, we performed parallel-evolution experiments using Escherichia coli cells under ethanol stress to determine the phenotypic changes necessary for ethanol tolerance.ResultsAfter cultivation of 1,000 generations under 5% ethanol stress, we obtained 6 ethanol-tolerant strains that showed an approximately 2-fold increase in their specific growth rate in comparison with their ancestor. Expression analysis using microarrays revealed that common expression changes occurred during the adaptive evolution to the ethanol stress environment. Biosynthetic pathways of amino acids, including tryptophan, histidine, and branched-chain amino acids, were commonly up-regulated in the tolerant strains, suggesting that activating these pathways is involved in the development of ethanol tolerance. In support of this hypothesis, supplementation of isoleucine, tryptophan, and histidine to the culture medium increased the specific growth rate under ethanol stress. Furthermore, genes related to iron ion metabolism were commonly up-regulated in the tolerant strains, which suggests the change in intracellular redox state during adaptive evolution.ConclusionsThe common phenotypic changes in the ethanol-tolerant strains we identified could provide a fundamental basis for designing ethanol-tolerant strains for industrial purposes.

Prediction of antibiotic resistance by gene expression profiles.

Although many mutations contributing to antibiotic resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to be fully elucidated. To better characterize phenotype–genotype mapping for drug resistance, here we analyse phenotypic and genotypic changes of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution. We demonstrate that the resistances can be quantitatively predicted by the expression changes of a small number of genes. Several candidate mutations contributing to the resistances are identified, while phenotype–genotype mapping is suggested to be complex and includes various mutations that cause similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances.

Comparison of sequence reads obtained from three next-generation sequencing platforms.

Next-generation sequencing technologies enable the rapid cost-effective production of sequence data. To evaluate the performance of these sequencing technologies, investigation of the quality of sequence reads obtained from these methods is important. In this study, we analyzed the quality of sequence reads and SNP detection performance using three commercially available next-generation sequencers, i.e., Roche Genome Sequencer FLX System (FLX), Illumina Genome Analyzer (GA), and Applied Biosystems SOLiD system (SOLiD). A common genomic DNA sample obtained from Escherichia coli strain DH1 was applied to these sequencers. The obtained sequence reads were aligned to the complete genome sequence of E. coli DH1, to evaluate the accuracy and sequence bias of these sequence methods. We found that the fraction of “junk” data, which could not be aligned to the reference genome, was largest in the data set of SOLiD, in which about half of reads could not be aligned. Among data sets after alignment to the reference, sequence accuracy was poorest in GA data sets, suggesting relatively low fidelity of the elongation reaction in the GA method. Furthermore, by aligning the sequence reads to the E. coli strain W3110, we screened sequence differences between two E. coli strains using data sets of three different next-generation platforms. The results revealed that the detected sequence differences were similar among these three methods, while the sequence coverage required for the detection was significantly small in the FLX data set. These results provided valuable information on the quality of short sequence reads and the performance of SNP detection in three next-generation sequencing platforms.

Comprehensive phenotypic analysis of single-gene deletion and overexpression strains of Saccharomyces cerevisiae.

We quantified the growth behaviour of all available single‐gene deletion and overexpression strains of budding yeast. Genome‐wide analyses enabled the extraction of the genes and identification of the functional categories for which genetic perturbation caused the change of growth behaviour. Statistical analyses revealed defective growth for 646 deletion and 1302 overexpression strains. We classified these deleted and overexpressed genes into known functional categories, and identified several functional categories having fragility and robustness for cellular growth. We also screened the deletion and overexpression strains that exhibited a significantly higher growth rate than the strain without genetic perturbation, and found that three deletion and two overexpression strains were high‐growth strains. The genes and functional categories identified in the analysis might provide useful information on designing industrially useful yeast strains. Copyright